Learn Data Science Courses in Bangalore

Fundamentals of Math and Probability

• Basic understanding of linear algebra, Matrics, vectors.
• Addition and Multiplication of matrices.
• Fundamentals of Probability
• Probability distributed function and cumulative distribution function.
• Class Hand-on Problem solving using R for vector manipulation.
• Problem solving for probability assignments.

The mean,median,mode, curtosis and skewness Computing Standard deviation and Variance.

Types of distribution.

Class Handson: 5 Point summary BoxPlot Histogram and Bar Chart Exploratory analytics R Methods.

What is inferential statistics Different types of Sampling techniques Central Limit Theorem.

Point estimate and Interval estimate.

Creating confidence interval for population parameter Characteristics of Z-distribution and T- Distribution.

Basics of Hypothesis Testing Type of test and rejection region Type of errors in Hypothesis resting.

Type-l error and Type-ii errors P-Value and Z-Score Method T-Test, Analysis of variance(ANOVA) and Analysis of Covariance(ANCOVA) Regression analysis in ANOVA.

Class Hands-on: Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Case study and model run for ANOVA.

Basics of Hypothesis Testing Type of test and Rejection Region

Type o errors-Type 1 Errors,Type 2 Errors

P value method,Z score Method. 

The Chi-Square Test of Independence Regression

Factorial Analysis of Variance

Pearson Correlation Coefficients in Depth Statistical Significance, Effect Size, and Confidence Intervals

Introduction to Data Cleaning

Data Preprocessing What is Data Wrangling?

How to Restructure the data? What is Data Integration?

Data Transformation

EDA : Finding and Dealing with Missing Values.

What are Outliers? Using Z- scores to Find Outliers. 

Introduction to Bivariate Analysis,Scatter Plots and Heatmaps.

 Introduction to Multivariate Analysis

Introduction To Machine Learning

What is Machine Learning? 

Introduction to Supervised and Unsupervised Learning

Introduction to SKLEARN (Classification, Regression, Clustering, Dimensionality reduction, Model selection, Preprocessing)

What is Reinforcement Learning?

Machine Learning applications

Difference between Machine Learning and Deep Learning

Support Vector Machines Linear regression

Logistic Regression Naive Bayes

Linear discriminant analysis Decision tree

k-nearest neighbor algorithm Neural Networks (Multilayer perceptron)

Similarity learning

 Introduction to Linear Regression

 Linear Regression with Multiple Variables

Disadvantage of Linear Models

Interpretation of Model Outputs

Understanding Covariance and Collinearity

Understanding Heteroscedasticity

Case Study – Application of Linear Regression for Housing Price Prediction

Introduction to Logistic Regression.

Why Logistic Regression .

Introduce the notion of classification

Cost function for logistic regression

Application of logistic regression to multi-class classification.

Confusion Matrix, Odds Ratio And ROC Curve

Advantages And Disadvantages of Logistic Regression.

Case Study:To classify an email as spam or not spam using logisticRegression.

Decision Tree – data set How to build a decision tree?

Understanding Kart Model

Classification Rules- Overfitting Problem

Stopping Criteria And Pruning 

How to Find the final size of Trees?

Model A Decision Tree.

Naive Bayes

Random Forests and Support Vector Machines

Interpretation of Model Outputs

Hierarchical Clustering

k-Means algorithm for clustering – groupings of unlabeled data points.

Principal Component Analysis(PCA)- Data

Independent components analysis(ICA) Anomaly Detection

Recommender System-collaborative filtering algorithm

Case Study– Recommendation Engine for e-commerce/retail chain

Introduction to Natural Language Processing (NLP).

Word Frequency Algorithms for NLP Sentiment Analysis

Case Study : Twitter data analysis using NLP

Basics of Time Series Analysis and Forecasting ,Method Selection in Forecasting

Moving Average (MA) Forecast Example,Different Components of Time Series Data ,Log Based Differencing, Linear Regression For Detrending

Introduction to ARIMA Models,ARIMA Model Calculations,Manual ARIMA Parameter Selection,ARIMA with Explanatory Variables

Understanding Multivariate Time Series and Their Structure,Checking for Stationarity and Differencing the MTS

Case Study : Performing Time Series Analysis on Stock Prices

Introduction to Deep Learning And TensorFlow

Neural Network

Understanding Neural Network

Model Installing

TensorFlow Simple Computation ,Constants And Variables

Types of file formats in TensorFlow

Creating A Graph – Graph Visualization

Creating a Model – Logistic Regression

Model Building using tensor flow

TensorFlow Classification Examples

Installing TensorFlow

Simple Computation , Contants And Variables

Types of file formats in TensorFlow

Creating A Graph – Graph Visualization

Creating a Model – Logistic Regression

Model Building

TensorFlow Classification Examples

Basic Neural Network Single Hidden Layer Model

Multiple Hidden Layer Model

Backpropagation – Learning Algorithm and visual representation

Understand Backpropagation – Using Neural

Network Example TensorBoard

Project on backpropagation

Convolutional Layer Motivation

Convolutional Layer Application
Architecture of a CNN

Pooling Layer Application

Deep CNN

Understanding and Visualizing a CNN

Introduction To RDBMS Single Table Queries – SELECT,WHERE,ORDER

BY,Distinct,And ,OR Multiple Table Queries: INNER, SELF, CROSS, and OUTER, Join, Left Join, Right Join, Full Join, Union.

Advance SQL Operations: Data Aggregations and summarizing the data

Ranking Functions: Top-N Analysis Advanced SQL Queries for Analytics.

Topics – What is HBase? HBase Architecture, HBase Components,

Storage Model of HBase, HBase vsRDBMS

Introduction to MongoDB, CRUD Advantages of MongoDB over RDBMS

Use cases

Mathematical Functions

Variables

Conditional Logic Loops

Custom Functions Grouping and Ordering Partitioning

Filtering Data Subqueries